A2C [1]} is a policy gradient algorithm that models the stochastic policy, \(\pi _\theta (s|a)\) , with a neural network. Essentially, the network parameters \(\theta \)  are obtained by optimizing for the objective function,
\(J_{a2c}(\theta ) = \hat{\mathbb {E}}_t\left[ \log \pi _{\theta }(a_t|s_t) \hat{A}_t \right],\) 
